SoroushVahidi/scidocs huggingface dataset:
Infrapatellar saphenous neuralgia - diagnosis and treatment.
(2013)

Persistent anterior knee pain, especially after surgery, can be very frustrating for the patient and the clinician. Injury to the infrapatellar branch of the saphenous nerve (IPS) is not uncommon after knee surgeries and trauma, yet the diagnosis and treatment of IPS neuralgia is not usually taught in pain training programs. In this case report, we describe the anatomy of the saphenous nerve and specifically the infrapatellar saphenous nerve branch; we also discuss the types of surgical trauma, the clinical presentation, the diagnostic modalities, the diagnostic injection technique, and the treatment options. As early as 1945, surgeons were cautioned regarding the potential surgical trauma to the IPS. Although many authors dismissed the nerve damage as unavoidable, the IPS is now recognized as a potential cause of persistent anterior and anteriomedial knee pain. Even more concerning, damage to peripheral nerves such as the IPS has been identified as the cause and potential perpetuating factor for conditions such as complex regional pain syndromes (CRPS). Because the clinical presentation may be vague, it has often been misdiagnosed and underdiagnosed. There is a documented vasomotor instability, but, unfortunately, sympathetic blocks will not address the underlying pathology, and therefore patients often will not respond to this modality, although the correct diagnosis can lead to rapid and gratifying resolution of the pathology. An entity unknown to the clinician is never diagnosed, and so it is important to familiarize pain physicians with IPS neuropathy so that they may be able to offer assistance when this painful condition arises.
The Timbre Model
(2001)

This paper presents the timbre model, a signal model which has been built to better understand the relationship between the perception of timbre and the musical sounds most commonly associated with timbre. In addition, an extension to the timbre model incorporating expressions is introduced. The presented work therefore has relation to a large field of science, including auditory perception, signal processing, physical models and the acoustics of musical instruments, music expression, and other computer music research. The timbre model is based on a sinusoidal model, and it consists of a spectral envelope, frequencies, a temporal envelope and different irregularity parameters. The paper is divided into four parts: an overview of the research done on the perception of timbre, an overview of the signal processing aspects dealing with sinusoidal modeling, the timbre model, and an introduction of some expressive extensions to the timbre model.
BreadCrumbs: forecasting mobile connectivity
(2008)

Mobile devices cannot rely on a single managed network, but must exploit a wide variety of connectivity options as they travel. We argue that such systems must consider the derivative of connectivity--the changes inherent in movement between separately managed networks, with widely varying capabilities. With predictive knowledge of such changes, devices can more intelligently schedule network usage.
 To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.
Tracking and Connecting Topics via Incremental Hierarchical Dirichlet Processes
(2011)

Much research has been devoted to topic detection from text, but one major challenge has not been addressed: revealing the rich relationships that exist among the detected topics. Finding such relationships is important since many applications are interested in how topics come into being, how they develop, grow, disintegrate, and finally disappear. In this paper, we present a novel method that reveals the connections between topics discovered from the text data. Specifically, our method focuses on how one topic splits into multiple topics, and how multiple topics merge into one topic. We adopt the hierarchical Dirichlet process (HDP) model, and propose an incremental Gibbs sampling algorithm to incrementally derive and refine the labels of clusters. We then characterize the splitting and merging patterns among clusters based on how labels change. We propose a global analysis process that focuses on cluster splitting and merging, and a finer granularity analysis process that helps users to better understand the content of the clusters and the evolution patterns. We also develop a visualization process to present the results.
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
(1993)

This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used by each node to keep accurate statistics on which routing decisions lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, Q-routing proves superior to a nonadaptive algorithm based on precomputed shortest paths and is able to route e ciently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically. The paper concludes with a discussion of the tradeo between discovering shortcuts and maintaining stable policies.
La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs
(1959)

I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 ~I. ]:~.ECONSTRUCTION PAR LES Cubi termes sp . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 I . La r e c o n s t r u c t i o n p r o p r e m e n t dire, p. 43. 2. Le c o m p o r t e m e n t en fonc t ion du groupe , p. 46. 3. Les condui tes individuel les , p. 49. 4. Les ac t iv i t4s au t r e s que la r econs t ruc t i on , p. 51. I I [ . RECONSTRUCTION PAR Bell icosi termes natalensis . . . . . . . . . . . . . . . . . . . . . 53 I V . ~V[]~CANISME DE LA CORRELATION DES TACIIES INDIVIDUELLES ET DE LA PRETENDUE REGULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 I . L a phase d ' incoordinat ion, p. 57. A. La s i tua t ion s t i m u l a n t e , p. 57 ; B. Les act iv i t~s incoordonn~es , p. 59 ; C. La local isa t ion des d~p6ts de t e r re , p. 60. 2. L a phase de coordinat ion, p. 61. A. La densi t6 cr i t ique des bou le t t e s de te r re et l ' o r i en ta t ion du c o m p o r t e m e n t , p. 61 ; B. La s t i g m e r g i e et les s t i m u l a t i o n s s imul tan6es , p. 62 ; C. N a t u r e des s t imu l i a g i s s a n t s u r les c o n s t r u c t e u r s : les odeurs fo rmes , p. 66 ; D. La t endance l ' un i t4 , p. 67 ; E, R61e de la m~moi re d a n s ia cons t ruc t ion , p. 67 ; F. Essa i de s y n t h ~ s e , p. 68. V. COMPORTEMENT DES TERMITES COMPARE A CELUI D'ANIMAUX SOLITAIRES. 73 VI . CARACTERISTIQUES FONDAMENTALES DU COMPORTEMENT DES INSECTES SOCIAUX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 V I I . AUTEURS CIT#.S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 L~.CENDES ~)ES PLANC~ES I A V I I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8'1
3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks
(2018)

Standard 3D convolution operations require much larger amounts of memory and computation cost than 2D convolution operations. The fact has hindered the development of deep neural nets in many 3D vision tasks. In this paper, we investigate the possibility of applying depthwise separable convolutions in 3D scenario and introduce the use of 3D depthwise convolution. A 3D depthwise convolution splits a single standard 3D convolution into two separate steps, which would drastically reduce the number of parameters in 3D convolutions with more than one order of magnitude. We experiment with 3D depthwise convolution on popular CNN architectures and also compare it with a similar structure called pseudo-3D convolution. The results demonstrate that, with 3D depthwise convolutions, 3D vision tasks like classification and reconstruction can be carried out with more light-weighted neural networks while still delivering comparable performances.
Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications
(2016)

Dynamic decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -aka quality propertiesand the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-time. In this paper we develop further our ideas on re-assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage.
Design of a Data-Driven PID Controller
(2009)

Since most processes have nonlinearities, controller design schemes to deal with such systems are required. On the other hand, proportional-integral-derivative (PID) controllers have been widely used for process systems. Therefore, in this paper, a new design scheme of PID controllers based on a data-driven (DD) technique is proposed for nonlinear systems. According to the DD technique, a suitable set of PID parameters is automatically generated based on input/output data pairs of the controlled object stored in the database. This scheme can adjust the PID parameters in an online manner even if the system has nonlinear properties and/or time-variant system parameters. Finally, the effectiveness of the newly proposed control scheme is evaluated on some simulation examples, and a pilot-scale temperature control system.
Blocked recursive image composition with exclusion zones
(2010)

Photo collages are a popular and powerful storytelling mechanism. They are often enhanced with background artwork that sets the theme for the story. However, layout algorithms for photo collage creation typically do not take this artwork into account, which can result in collages where photos overlay important artwork elements. To address this, we extend our previous Blocked Recursive Image Composition (BRIC) method to allow any number of photos to be automatically arranged around preexisting exclusion zones on a canvas (exBRIC). We first generate candidate binary splitting trees to partition the canvas into regions that accommodate both photos and exclusion zones. We use a Cassowary constraint solver to ensure that the desired exclusion zones are not covered by photos. Finally, photo areas, exclusion zones and layout symmetry are evaluated to select the best candidate. This method provides flexible, dynamic and integrated photo layout with background artwork.
Data Quality and Data Cleaning: An Overview
(2003)

Data quality is a serious concern in any data-driven enterprise, often creating misleading findings during data mining, and causing process disruptions in operational databases. The manifestations of data quality problems can be very expensive- "losing" customers, "misplacing" billions of dollars worth of equipment, misallocated resources due to glitched forecasts, and so on. Solving data quality problems typically requires a very large investment of time and energy -- often 80% to 90% of a data analysis project is spent in making the data reliable enough that the results can be trusted.In this tutorial, we present a multi disciplinary approach to data quality problems. We start by discussing the meaning of data quality and the sources of data quality problems. We show how these problems can be addressed by a multidisciplinary approach, combining techniques from management science, statistics, database research, and metadata management. Next, we present an updated definition of data quality metrics, and illustrate their application with a case study. We conclude with a survey of recent database research that is relevant to data quality problems, and suggest directions for future research.
An improved analytical IGBT model for loss calculation including junction temperature and stray inductance
(2015)

An improved analytical model suitable for IGBT modules is proposed in this paper to calculate the power losses with high accuracy and short calculation time. In this model, the parameters varying with junction temperature of the modules, such as di/dt in the turn-on period and dv/dt in the turn-off period, are discussed and derived according to several equivalent models. In addition, the parasitic inductance in the circuit including the emitter and collector inductance in the power circuits and the gate inductance in the driving loop are considered in this model. Based on this proposed model, the simulation switching waveforms of collector currents and collector-emitter voltages are provided to verify the model. Meanwhile, the calculated power losses are confirmed to be precise by comparing with measurement results.
Temporal Segmentation of Egocentric Videos
(2014)

The use of wearable cameras makes it possible to record life logging egocentric videos. Browsing such long unstructured videos is time consuming and tedious. Segmentation into meaningful chapters is an important first step towards adding structure to egocentric videos, enabling efficient browsing, indexing and summarization of the long videos. Two sources of information for video segmentation are (i) the motion of the camera wearer, and (ii) the objects and activities recorded in the video. In this paper we address the motion cues for video segmentation. Motion based segmentation is especially difficult in egocentric videos when the camera is constantly moving due to natural head movement of the wearer. We propose a robust temporal segmentation of egocentric videos into a hierarchy of motion classes using a new Cumulative Displacement Curves. Unlike instantaneous motion vectors, segmentation using integrated motion vectors performs well even in dynamic and crowded scenes. No assumptions are made on the underlying scene structure and the method works in indoor as well as outdoor situations. We demonstrate the effectiveness of our approach using publicly available videos as well as choreographed videos. We also suggest an approach to detect the fixation of wearer's gaze in the walking portion of the egocentric videos.
Compact and power-efficient 5 GHz full-duplex design utilizing the 180° ring hybrid coupler
(2017)

This paper presents a compact and power-efficient 5 Ghz in-band full-duplex (FD) design in ANSYS HFSS using the 180-degree ring hybrid coupler. The proposed design achieves an excellent isolation of 57dB by taking advantage of destructive interference between two radiating antennas attached to the coupler, leading to a large reduction in self-interference. The design is passive and hence overcomes additional power requirement for adaptive channel estimation. In addition, it has a very workable physical size for the desired frequency of operation. The proposed FD design is therefore compact and power-efficient, which can be used in mobile devices, such as cell phones or tablet/phablet devices for a more flexible and greater contention of scarce RF resources.
TAaaS: Trustworthy Authentication as a Service Based on Trusted Path
(2016)

Authentication as a Service (AaaS) provides on-demand delivery of multi-factor authentication (MFA). However, current AaaS has left out of consideration the trustworthiness of user inputs at client evices and the risk of privacy exposure at the AaaS providers. To solve these concerns, we present TAaaS, Trustworthy Authentication as a Service, which offers a trusted path-based MFA service to the service provider in the cloud. TAaaS leverages the hypervisor-based trusted path to ensure the trustworthiness of user inputs, and addresses privacy concerns in the cloud by storing only the irreversible user account information. We implement two end-to-end prototypes and evaluate our work to show its feasibility and security.
OFDM : Basic Concepts , Scope & its Applications
(2015)

Orthogonal frequency division multiplexing (OFDM) is a special case of multicarrier transmission where a single DataStream is transmitted over a number of lower rate subcarriers. In July 1998, the IEEE standardization group decided to select OFDM as the basis for their new 5-GHz standard aiming a range of data stream from 6 up to 54 Mbps. This new standard is the first one to use OFDM in packetbased communications. In wireless communication, concept of parallel transmission of symbols is used to achieve high throughput and better transmission quality. Orthogonal Frequency Division Multiplexing (OFDM) is one of the techniques for parallel transmission. The idea of OFDM is to split the total transmission bandwidth into a number of orthogonal subcarriers in order to transmit the symbols using these subcarriers in parallel. In this paper we will discuss the basics of OFDM techniques, role of OFDM in this era, its benefits and losses and also some of its application. Keywords— Orthogonal frequency division multiplexing(OFDM); BER; ISI; PAPR; DVB; DAB.
Graduated Consistency-Regularized Optimization for Multi-graph Matching
(2014)

Graph matching has a wide spectrum of computer vision applications such as finding feature point correspondences across images. The problem of graph matching is generally NP-hard, so most existing work pursues suboptimal solutions between two graphs. This paper investigates a more general problem of matching N attributed graphs to each other, i.e. labeling their common node correspondences such that a certain compatibility/affinity objective is optimized. This multigraph matching problem involves two key ingredients affecting the overall accuracy: a) the pairwise affinity matching score between two local graphs, and b) global matching consistency that measures the uniqueness and consistency of the pairwise matching results by different sequential matching orders. Previous work typically either enforces the matching consistency constraints in the beginning of iterative optimization, which may propagate matching error both over iterations and across different graph pairs; or separates score optimizing and consistency synchronization in two steps. This paper is motivated by the observation that affinity score and consistency are mutually affected and shall be tackled jointly to capture their correlation behavior. As such, we propose a novel multigraph matching algorithm to incorporate the two aspects by iteratively approximating the global-optimal affinity score, meanwhile gradually infusing the consistency as a regularizer, which improves the performance of the initial solutions obtained by existing pairwise graph matching solvers. The proposed algorithm with a theoretically proven convergence shows notable efficacy on both synthetic and public image datasets.
Mental Stress Assessment of ECG Signal using Statistical Analysis of Bio-Orthogonal Wavelet Coefficients
(2013)

It is observed that the stress level is function of various statistical parameters like standard deviation, entropy, energy,, mean, Covariance and power of the ECG signals of two states i.e. normal state of mind and stressed state of mind. Further, it is observed that the features extracted are directly from the ECG in frequency domain using db4 wavelet. However, db4 introduces some error on account of db4 wavelet shape. This error in turn amplifies while measuring the features as mentioned above. In order to reduce this error, we propose a Bior 3.9 wavelet family to decompose the ECG signal. The decomposed ECG signal is now analyzed statistically to extract the above feature. Further, in the existing work, all the ECGs are taken by using the 12 leads method. This factor also adds some undue stress to the person under scanner. And this is time consuming too. Therefore, to reduce this complexity, we propose to analyze all above features by using a two lead ECG. Further, a back propagation neural network is trained using the above features as input neurons in order to classify the stress level.
A Better Comparison Summary of Credit Scoring Classification
(2017)

The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The credit risk analysis is more effective with further boosting and smoothing of the parameters of models. The objective of this paper is to explore the credit score classification models with an imputation technique and without imputation technique. However, data availability is low in case of without imputation because of missing values depletion from the large dataset. On the other hand, imputation based dataset classification accuracy with linear method of ANN is better than other models. The comparison of models with boosting and smoothing shows that error rate is better metric than area under curve (AUC) ratio. It is concluded that artificial neural network (ANN) is better alternative than decision tree and logistic regression when data availability is high in dataset. Keywords—Credit score data mining; classification; artifical neural network; imputation
How to Predict Mood? Delving into Features of Smartphone-Based Data
(2016)

Smartphones are increasingly utilized in society and enable scientists to record a wide range of behavioral and environmental information. These information, referred to as Unobtrusive Ecological Momentary Assessment Data, might support prediction procedures regarding the mood level of users and simultaneously contribute to an enhancement of therapy strategies. In this paper, we analyze how the mood level of healthy clients is affected by unobtrusive measures and how this kind of data contributes to the prediction performance of various statistical models (Bayesian methods, Lasso procedures, etc.). We conduct analyses on a non-user and a user level. We then compare the models by utilizing introduced performance measures. Our findings indicate that the prediction performance increases when considering individual users. However, the implemented models only perform slightly better than the introduced mean model. Indicated by feature selection methods, we assume that more meaningful variables regarding the outcome can potentially increase prediction performance.